Sports and concert event ticket pricing and visualization system

ABSTRACT

A system and method for selecting inventory pricing for an event at a venue is disclosed. The method comprising determining a rate at which a first inventory of seats have sold for an event at a venue. The method further comprising calculating a demand for a second inventory of seats as a function of the rate at which the first inventory of seats sold, the seats of the first and second inventories being comparable in quality. provides a user interface to one or more client devices that displays the data. The method further comprising calculating a demand for a second inventory of seats as a function of the rate at which the first inventory of seats sold, the seats of the first and second inventories being comparable in quality.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application is a divisional application of now-allowed U.S.patent application Ser. No. 12/422,171, which is a continuation ofInternational Application No. PCT/US/09/35024, filed Feb. 24, 2009,which claims priority to and is a non-provisional application of U.S.Provisional Patent Application Ser. No. 61/031,020, filed Feb. 25, 2008(Attorney Docket No. 027713-000300US), U.S. Provisional PatentApplication Ser. No. 61/055,142, filed May 22, 2008 (Attorney Docket No.027713-000100US), U.S. Provisional Patent Application Ser. No.61/098,765, filed Sep. 20, 2008 (Attorney Docket No. 027713-000200US),and U.S. Provisional Patent Application Ser. No. 61/114,463, filed Nov.14, 2008 (Attorney Docket No. 027713-000400US), the disclosures of whichare each hereby incorporated by reference in their entirety for allpurposes.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present application generally relates to data processing infinancial, business practice, management, or cost/price determination inreservation, check-in, and booking display for reserved space. Systemsand methods for sales, pricing, and distribution of tickets for concert,sports, and other events are presented. More specifically, the presentinvention relates to a system and method for facilitating the pricing oftickets at a venue prior to the event, displaying seat inventory at thevenue, determining demand for tickets before, during, and after aninitial on-sale, and automatically determining if prices should bechanged or inventory should be redirected to a different distributionchannel based on the demand for the tickets. The present applicationalso relates to determining optimally valued tickets for purchase by aconsumer and determining the appropriate customer for that ticket.

2. Description of the Related Art

Computer systems and networks have facilitated the task of buying,selling, and transferring goods. For example, global computer networks,such as the Internet, have allowed purchasers to quickly and efficientlyseek and purchase goods on-line. Buying and selling tickets online toevents at sports stadiums, arenas, theaters, entertainment clubs, andother venues has become a multi-billion dollar industry.

The accurate pricing of tickets is sometimes critical to achieve maximumrevenues for an event. Prices that are too high will curb demand, whileprices that are too low will create ample demand but at a non-optimalprice. For sports teams, prices are typically determined at thebeginning of a season, and pricing adjustments are made throughpromotions, give-aways, or other mechanisms. However, the face value ofthe tickets often remains unchanged. For concerts and other “one-off”events, pricing can be independent of other events.

The relative pricing of tickets within a venue is also a challenge.Currently, prices are typically determined by their ‘section’ in avenue. The closer, more centered, and less obstructed the view of theevent from the section, the better the seat and the higher the ticketprice. Seats with comparable views can be considered seats of comparableseat quality. In some venues, floor tickets in a given section will bepriced higher than the rest of the seats in that section. Today, thesepricing decisions are typically made based on personal experience ofpromoters, venue representatives, and other industry professionals.

In a venue, a ‘seat’ is not necessarily a chair, bench, or otherapparatus upon which one sits down. Instead, a seat can include an openspace for a wheelchair, stroller, or similar conveyance, a position in ageneral standing area, a place to bring one's own chair and picnicbasket, or other definitions as known in the art. A seat can alsoinclude a parking place for drive-in theater, a dock along a log-boomfor watching a hydroplane race, a rail upon which to tie up a horse, orother positions upon which a vehicle or conveyance can be parked,anchored, or moored. For clarity and simplicity in explanation,individual seats will be referred to in the examples of thisspecification, although the broader term is certainly envisioned.

There are many types of revenue management challenges in sports andentertainment planning, including how to price events. Pricing an eventcommonly occurs in advance of the beginning of ticket sales, but canalso occur over time during the sale period of tickets and even duringthe beginning of the event. Other challenges related to pricing includedeciding what discounts should be offered or what premiums should becharged, to whom and when to offer or charge the discounts or premiums,and issues around grouping events into packages. Ticket packages caninclude multiple rickets for different events at the same venue anddifferent events at different venues. Packages can also include ticketssold for the same event at the same venue for large parties, for examplegroup discounts.

Selling events in bundles as season or partial-season tickets is animportant revenue management area, especially for sporting events.Customers who purchase bundles are committing to multiple events, but atreduced per-event ticket prices. The customers are assured that theywill be able to attend events throughout the season. Season tickets mayyield revenue benefits for the selling organization, such as early cashflow and reduced risk. Thus, bundled ticket products are prioritized andare generally sold first in the selling season, while individual ticketsfor those events are made available at a later date. Determining theproper mix of bundled sales and single seat sales can be an importantdecision.

Often, pricing is determined based on a total or net revenue targetassociated with the event rather than on demand for that particularevent. The pricing of events often involves the use of a spreadsheetwhich contains the number of seats in each section. Prices for eachsection are estimated and added to the spreadsheet, and the totalrevenue is calculated by multiplying the section price by the totalseating capacity for that section. Pricing is altered until a certaintargeted total revenue is met. From the spreadsheet, a venue map is thencolored by hand to give a visual representation of the seatingarrangement and corresponding price levels. The tradeoff betweensections, pricing levels, and other factors is tracked in the minds ofthe venue representatives and promoters. The ability to visualize theseating arrangements in a venue map is not directly linked to theability to calculate financial information.

Ideally, prices would be established before an event takes place andthese prices would never need to be altered. However, it may also bedesirable to change prices once an event has gone on sale. If theinitial indication is that demand is greater than expected, it would bedesirable to raise prices. If demand is lower than expected, it may bedesirable to lower prices.

Recently there has been a growing interest in revenue managementsystems. This is particularly true for perishable products. A perishableproduct is one where the item has no value beyond a certain date. Oneobvious example is a food product susceptible to spoilage, but hotelrooms, airline seats, and event tickets are also examples of perishableproducts. See U.S. Pat. No. 7,020,617 issued Mar. 28, 2006 to Ouimertand U.S. Pat. No. 6,078,893 issued Jun. 20, 2000 to Ouimert et al., bothhereby incorporated by reference for all purposes. Airline seats andhotel rooms are particularly of interest in some recent studies. SeeU.S. Pat. No. 6,993,494 issued Jan. 31, 2006 to Boushy et al. and R.Preston McAfee and Vera to “Dynamic Pricing in the Airline Industry,”(Pasadena, Calif.: California Institute of Technology, undated), 44pages. These systems use past history and current inventory data tomanage revenue and profit.

More recently, there has been an attempt to apply these types of systemsto sports events and concerts. See U.S. Pat. No. 7,110,960 issued Sep.19, 2006 to Phillips et al., which is hereby incorporated for allpurposes. See also Welki, Andrew M. and Thomas J. Zlatoper, “USProfessional Football: The Demand For Game-Day Attendance in 1991,”Managerial and Decision Economics, Vol. 15, Issue 5, Special Issue: TheEconomics of Sports Enterprises (September-October, 1994) (New York:John Wiley & Sons, 1994), pages 489-495. See also Drake, M. J., S.Duran, P. M. Griffin, and J. L. Swann, “Optimal timing of switchesbetween product sales for sports and entertainment tickets,” NavalResearch Logistics, Vol. 55, Issue 1, (New York: Wiley Periodicals,Inc., 2007), pp. 59-75. Determining pricing for sports events issometimes more challenging than pricing airline seats and hotel roomsbecause there is more consistency in the airline or hotel industry. Forexample, typically an airline will fly the same plane the same day ofthe week at the same time to the same destination. Past history is agood indicator of future demand. In sports, however, demand is dependenton many factors including the opponent, the day of the week, if a playergets injured, or even the weather. Most past effort has focused onestablishing the proper relationship between the many variables anddemand. These systems can be very complex; thus, demand is stilltypically estimated by sales and marketing personnel based on their ownpast experience and intuition. This challenge is further complicated asthe value of a ticket is also dependent on the location within the venue(as compared to an airline where all coach seats traditionally areconsidered of equal value).

Pricing for concerts and other “one-off” events can be more challengingthan sporting events where the same team may play multiple games in thesame venue. For these “one-off” events like concerts, boxing matches,ice shows, etc., pricing is often determined by targeting a specifictotal revenue assuming some portion of the seats sell. Promoters may usepast history to estimate demand, but often this data is old, andcustomer preference, economic factors, and other issues impacting demandmay have changed significantly for the current event relative to demandfor a prior event.

One key to all of these challenges is being able to determine demand forthe event, and then converting this demand into a fair price fortickets. Historically, tickets were only sold once, although there hasalmost always been “scalping,” or the ability to sell a ticket in theaftermarket. A recent proliferation of secondary marketing companies,particularly those that sell tickets on the web, has greatly increasedthe number of tickets that are resold. The availability of tickets inthe aftermarket has important implications for the sale of originaltickets. For example, tickets selling for a discount in the secondarymarket will negatively impact the sale of full price tickets in theprimary market. The original, or primary, ticket market encompasses allinstances in which event tickets are sold for the first time. Thesecondary ticket market encompasses all instances in which event ticketstrade after the original point of purchase.

Original and secondary event ticket markets are known in the art. SeeU.S. Patent Application Pub. No. 2006/0095344 published May 4, 2006 forNakfoor and U.S. Patent Application Pub. No. 2004/0093302 published May13, 2004 for Baker et al., both hereby incorporated for all purposes.

There are a large number of secondary ticketing sites that enable thepurchase of resold tickets through the Internet. Customers looking topurchase the ticket with the best overall value typically must browsefrom site to site and manually compare listings, both within one siteand across multiple sites.

Once pricing is established, effective marketing of those tickets to theright customer poses another challenge. Determining which potentialcustomers are most likely to purchase a specific type of product,whether in the primary or secondary market, can be difficult given thewide range of customers and varying and ever-changing interests of thepublic. Typically, customer analysis is done across all customers, butthe exact nature of customer interest in an event may depend not only onthe event but also on how much the customer is willing to pay for aticket to that event. The customer profile may also depend on where thecustomer wants to sit in the venue.

Thus, there is a need for a system that is capable of more accuratelyforecasting demand for events and optimizing pricing for that event.There is also a need for this system to facilitate the price planningand inventory tracking process for events. There is also a need for thissystem to provide recommended price changes once an event goes on sale.There is a further need for this system to be able to correlate thedemand to a specific customer demographic to aid in the marketing forthe event. Finally, it is desirable for this information to be viewed ina format that is easy to interpret.

There is also a need to clearly display available inventory to potentialpurchasers of primary and secondary seats where the value of prospectiveseats is also clearly displayed.

BRIEF SUMMARY OF THE INVENTION

Embodiments in accordance with the present disclosure relate toplanning, editing, tracking, recommending, and determining pricing anddistribution channels for event tickets. During the planning stages ofan event, an embodiment can (1) determine an estimate of pricing fromexternal data and (2) determine pricing for recurring events (e.g.,sports seasons). Pricing is determined by analyzing secondary marketdata, web traffic, and other variables. Another embodiment can (3) use aprice planning software tool to visualize and edit pricing. Price levelsare correlated to various seats for an event at a venue using a venuemap in a web-based environment in order to track potential revenuedynamically during the pricing process. Another embodiment (4) tracksinventory once the event goes on sale, either by (4a) current snap shotsor by (4b) sales over time. Inventory status is managed visually in thesame web-based environment by accessing an inventory database. Changesin inventory over time for an event can be visualized in graphical form,or a movie of how inventory changes with time can be created andanalyzed to determine current and future demand patterns. Demand can bedetermined using multiple methods, including gathering and analyzingprices for comparable seats in the secondary markets; determining salesvelocity in the primary market, and analyzing the correlation ofinquiries and seats sold. Yet another embodiment can (5) makerecommendations for pricing and distribution based on data around theon-sale. Prices can be adjusted accordingly to an analysis of the datato maximize revenues. The price of certain seats, rows, or sections canbe (5a) increased or decreased (‘flexed’) based on demand, or (5b)tickets can be redirected to the secondary market to increase revenues.These pricing mechanisms provide a means to dynamically match priceswith demand. Further, some embodiments include (6) an improved systemfor presenting secondary inventory for purchase. A further embodimentcan (7) match available inventory to the proper customer.

The numbering in the above paragraph and section headings below areadded for clarity an are not intended in any way to delineate featuresor aspects of the invention which must be represented in an embodiment.Many features are disclosed in this specification which may or may notfall within the scope of the headings. One should refer to the claims asissued in a patent by the Patent Office to determine the metes andbounds of the invention and use the entire disclosure to determine thelegal equivalents therein.

One embodiment in accordance with the present disclosure relates to acomputerized method for determining prices for an event at a venuehaving seats, the method comprising modeling two or more externalvariables including but not limited to web site traffic, radio playtime, prior sales, size of the city or region where the event will takeplace, venue size, the demographics of the city where the event will beheld, and the demographics of the customers that frequent the planned orsimilar events. The computerized method fits sales for a prior knownevent with one or more external variables to a mathematical model anddetermines pricing for a future event based on the values of the sameexternal variables for said future event.

Another embodiment relates to a computerized method for determiningticket packages for events, the method comprising determining, usingprincipal components analysis, an event quality for each of multipleevents, calculating an average event quality of the multiple events, andgrouping the events into two or more groups. The events are grouped intogroups of like event qualities. The method further includes packagingone or more events from one of the groups with one or more events fromanother of the groups such that an average of the packaged events issubstantially equal to the average event quality of the multiple events.“Substantially equal averages” are those which are generally equal,including those which are within ±10%, ±25%, or greater of each other.

Another embodiment relates to a computerized method for determiningpricing for multiple similar events, the method comprising receiving alist of seat tickets and corresponding prices for sale on a secondarymarket, filtering the list to remove outliers, determining the pricingor premium of the secondary market inventory for multiple similarevents, fitting the price or premium for each event to the total revenuegenerated for that event to a mathematical model, and determiningrevenue for a future event not used in the model based on the secondarypricing or premium for that event.

Another embodiment in accordance with the present disclosure relates toa computerized method for price planning an event at a venue havingseats, the method comprising providing price levels for an event at avenue, receiving rules to attribute the price levels to the seats, andcorrelating each price level with the seats according to the rules. Themethod further comprises displaying the price levels correlated with theseats on a venue map and calculating a total projected revenue for theevent using the price levels and a number of seats correlated with eachprice level.

Another embodiment relates to a computerized method for tracking anddisplaying a seat inventory of an event on a venue map, the methodcomprising providing a map of a venue, the map having graphics depictingseats, receiving a sales status of the seats from a database, anddisplaying the sales status of each seat with the corresponding seatgraphic on the map.

Another embodiment relates to a computerized method for tracking anddisplaying a seat inventory of an event on a venue map, the methodcomprising receiving a first status of an inventory of seats at a firstpoint in time, displaying the first status of the inventory of seats ona venue map, and receiving a second status of the inventory of seats ata second point in time. The method also includes updating the venue mapwith the second status of the inventory of seats on the venue map. Asequence of such updates may result in a movie.

Another embodiment relates to a computerized method for tracking anddisplaying a seat inventory of an event on a venue map, the methodcomprising receiving a first status of an inventory of seats at a firstpoint in time, receiving a second status of the inventory of seats at asecond point in time and providing a chart or graph of the inventory asa function of time.

Another embodiment relates to a computerized method for selectinginventory pricing for an event at a venue, the method comprisingdetermining a rate at which a first inventory of seats have sold for anevent at a venue and calculating a demand for a second inventory ofseats based on an algorithm which uses the rate at which the firstinventory of seats sold. The seats of the first and second inventoriesare comparable in quality. The method also includes determining one of aplurality of price levels at which to release the second inventory ofseats using the demand.

Another embodiment relates to a computerized method for selectinginventory pricing for an event at a venue, the method comprisingreceiving a first sales status for a first plurality of seats for anevent at a first time point, the first plurality of seats having a firstprice level, receiving a second sales status for the first plurality ofseats at a second time point, and calculating the number of seats thatwill be sold at some time in the future by analyzing the first salesstatus and the second sales status. The method further includesalgorithmically predicting a number of seats that could be moved from asecond price level to the first price level based on the predicteddemand of tickets at the first price level.

Another embodiment relates to a computerized method for determining thevalue of an available inventory of seats to an event if sold in thesecondary ticket market, the method comprising receiving a list of seattickets and corresponding prices listed for sale on a secondary market,grouping the seat tickets in the list by equivalent sections of seats,filtering the list to remove outliers, and fitting the prices of one ofthe groups of seat tickets as a function of row to a mathematical model.The method further includes calculating a potential price for seats ineach row from the mathematical model and determining whether inventoryof unsold seat tickets is priced lower than the calculated potentialprice. Still further, the method includes filtering the inventory ofprimary seat tickets based on the determination of whether the seat ispriced lower than the calculated potential secondary price anddisplaying the inventory that could achieve higher prices in thesecondary market than in the primary market.

Another embodiment relates to a computerized method for displayingavailable inventory of seats to an event for sale in the secondaryticket market, the method comprising receiving a list of seat ticketsfor sale on a secondary market, grouping the seat tickets in the list byequivalent sections of seats if desired, and determining one or morebest valued seats based on the seat price relative to the mathematicalmodel.

Another embodiment relates to a computerized method of determininglikely customers for a particular event ticket, the method comprisingassociating demographic descriptors to customers of a similar priorevent, correlating the demographic data to ticket price paid for theprior event, thereby determining the demographic profile of customers ofa future event as a function of ticket price.

Yet other embodiments relate to systems and machine-readable tangiblestorage media which employ or store instructions for the methodsdescribed above.

A further understanding of the nature and the advantages of theembodiments disclosed and suggested herein may be realized by referenceto the remaining portions of the specification and the attacheddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a web page of a secondary market web site.

FIG. 2 illustrates a second web page of the secondary market web site ofFIG. 1.

FIG. 3 is a flowchart with operations in accordance with an embodiment.

FIG. 4 illustrates secondary market discounts/premiums of differentevents plotted versus total revenue.

FIG. 5 is a plot of secondary market discounts/premiums for variousevents versus total revenues for the events.

FIG. 6 is a plot of secondary market discounts/premiums for variousevents versus projected revenues for the events.

FIG. 7 illustrates a web page for defining price levels in accordancewith an embodiment.

FIG. 8A illustrates a web page for correlating price levels withsections of seats in accordance with an embodiment.

FIG. 8B illustrates a web page for correlating price levels withmultiple sections of seats in accordance with an alternate embodiment.

FIG. 9 illustrates a venue map showing price levels correlated withseats in FIG. 8A in accordance with an embodiment.

FIG. 10A illustrates a web page for editing price codes in accordancewith an embodiment.

FIG. 10B illustrates a web page for creating price codes in accordancewith the embodiment of FIG. 10A.

FIG. 10C illustrates a web page for viewing price codes and a relatedvenue map in accordance with the embodiment of FIG. 10A.

FIG. 11 is a flowchart with operations in accordance with an embodiment.

FIG. 12A illustrates a venue map showing statuses of inventories ofseats in accordance with an embodiment.

FIG. 12B illustrates an enlarged view of a portion of FIG. 12A.

FIG. 12C illustrates the venue map of FIG. 12A updated with statuses ofinventories of seats in accordance with an embodiment.

FIG. 12D illustrates the venue map of FIG. 12A showing allocations ofinventories of seats in accordance with an embodiment.

FIG. 13 is a flowchart with operations in accordance with anotherembodiment.

FIG. 14 is a plot of tickets sold and inquiries with respect to time inaccordance with one embodiment of the present invention.

FIG. 15 is a plot of total ticket sales for an event versus time.

FIG. 16 is a logarithmic plot of ticket sales per minute for an eventversus time.

FIG. 17 is a flowchart with operations in accordance with an embodiment.

FIG. 18 is a plot of tickets sold on the 100 level of a stadium withrespect to time in accordance with one embodiment of the presentinvention.

FIG. 19 is a plot of tickets sold versus number of inquires at differenttimes for a particular event that can be used in accordance with oneembodiment of the present invention.

FIG. 20 illustrates a web page with final recommendations according toan embodiment.

FIG. 21A illustrates a table showing data and recommendations using analgorithm according to an embodiment.

FIG. 21B illustrates a table showing data and recommendations using thesame algorithm as in FIG. 21A.

FIG. 22A is a plot of secondary market discounts/premiums of specifictickets versus row number.

FIG. 22B is a table of the data plotted in FIG. 22A.

FIG. 23 is a plot of historical secondary market discounts/premiums foran event versus row number.

FIG. 24A is a plot of historical secondary market discounts/premiums ofside sections for an event versus row number.

FIG. 24B is a plot of historical secondary market discounts/premiums ofa center section for the same event as in FIG. 24A versus row number.

FIG. 25 is a flowchart with operations in accordance with an embodiment.

FIG. 26 illustrates a venue map showing seats available on a consumerweb site in accordance with an embodiment.

FIG. 27 illustrates components of a computer network that can be used inaccordance with one embodiment of the present invention.

FIG. 28 illustrates components of a computerized device that can be usedin accordance with one embodiment of the present invention.

The figures will now be used to illustrate different embodiments inaccordance with the invention. The figures are specific examples ofembodiments and should not be interpreted as limiting embodiments, butrather exemplary forms and procedures.

DETAILED DESCRIPTION OF THE INVENTION (1) Determining Prices

Planning for ticket prices for event or series of events, such asbasketball games, occurs weeks, months, or even years before the firstticket to the event is sold. Much of the planning is performed bypromoters and representatives of the venue where the event will be held.Other experienced professionals and stakeholders in the event industrymay also help in price planning.

In planning for an event, promoters and venue representatives determinewhat seats will be made available in the venue as inventory. In hashingout what ticket price levels (i.e., dollar amounts) to employ and whichseats should be sold at what price levels, the promoter and venuerepresentatives weigh a large amount of data relating to historicalprices, seating layouts, restricted areas, and setup eccentricitiescorresponding to the event at issue. Much of this is performed in theminds of the representatives based on experience with similar events or“last season's” prices. There are a multitude of variables to beconsidered in light of mass consumer preferences and expectations, notthe least of which are the layout of seating for the particular eventbeing planned.

Venues can typically be rearranged between different types of events,such as between sporting events and concert events. For example, asporting event may call for all seats to be available in the stands andno seats available on the field, while a concert event may fill thefloor of the arena with seats and kill or otherwise restrict from saleseats behind a stage. Some sporting events have different seatinglayouts than others. For example, basketball, which features seats nearthe ground and close to the action, typically has a different seatingarrangement than football, which features seats farther away from thefield. Some events, such as soccer and football, may have equal seatingarrangements. Likewise, different concerts and entertainment events mayhave similar or different layouts, depending on the artists involved,types of performance, or types of production. Seating layouts may bepredetermined by the promoter, venue, or artist, or the seating layoutmay be determined in conjunction with pricing.

Often, the first step in planning an event is to determine pricing.Sometimes there is not adequate data associated with the event toprovide accurate pricing. For example, a particular band may not havetoured for several years. In this case, data for a similar act can beused to augment the information. The similar act can be determined bycomparing key variables of the act of interest with those of otherbands. Key variables may include the type of music the band performs aswell as the audience demographics the band attracts. These variables canassociated with a cardinal or ordinal value and be compared usingclustering software, such as principal component analysis (PCA). In thisway, past events similar to those of the current event can bequantitatively recognized and presented.

After past events are recognized, revenue and other demand-indicatingdata from those events can be analyzed to estimate current demand forthe event being planned.

A mathematical or computerized simulation model can be used to estimatethe demand. Key variables that can be incorporated into the model mayinclude venue size, demographics of the act's fans, demographics of thecity for the event, the population of the city or area where the eventis to be played, the number of Internet searches for the artist from thecity or area where the act has played and plans to play, the frequencywith which the artist's music is played on the radio, and the number ofother events that occurred or are planned to occur at the same time andin the same city of the event of interest.

In one analysis performed by the inventors, the most important variablesto determine demand were determined to be total population of the cityfor the event, the number of on-line searches for the artist in the cityof the event, and the radio play time for the artist in the city of theevent. By fitting these variables to total concert revenue in citieswhere the act or comparable act had already performed, a model wasgenerated and then applied to cities where the act had not yetperformed. By using the population, search information, and radio playtime for these new cities, a projected revenue was calculated. Thistotal revenue was then used to determine average ticket pricing based ondifferent seating configurations (i.e., projected revenue/number ofseats=price per seat).

Thus, determining demand for an event such as a concert can beanalytically determined. Such a determination may be proceduralized andthen optimized to create the best pricing levels for an event.

(2) Pricing Multiple Events—Season Tickets and Packages

Pricing multiple events, such as a season of football games, presents adifferent set of challenges than pricing for individual events. Forsports events that feature a whole season of home games played at thesame venue, season tickets and group packages complicate demand andpricing models because of cross-coupling between games at differenttimes of the year, different nights of the week, and playoff and regularseason games.

At times it may be helpful to create groupings of events based on thedesirability of the events or so-called ‘event quality.’ This can beused in the creation of bundling plans where highly desirable events arecombined with less desirable events to create mini-season ticket plans.This is particularly true for sports teams that play multiple games eachseason. In order to properly group events, it is often important toestablish the relative value for each event or game. One method todefine event quality is by using a clustering analysis such ashierarchical cluster analysis or principal components analysis (PCA).This technique allows demand oriented variables to be analyzedsimultaneously to provide an indication of similarity across theseevents. Key variables could include historical data such as totalrevenue, group revenue or sales on the day prior to the event from thepreceding year. The PCA can also use additional non-revenue basedinformation that may impact revenues, such as changes in personnel sincethe revenue data was collected, etc. These factors can sometimes createa significant change in event desirability or game quality. Principalcomponent analysis is then used to group these events in terms ofdesirability. This will result in two or more groups. Packages can thenbe created by taking single events from the different desirabilitylevels. For example, a highly desirable event can be combined with aless desirable event to create a package.

The secondary market can also play an important role in indicating eventdesirability or game quality. In fact, it is sometimes possible to drawa direct correlation between secondary premium and total revenue for anevent. The event desirability or projected total revenue can be used todetermine pricing for the events.

An analytical correlation between ticket prices in the secondary marketand future ticket sales has been developed by the inventors. If ticketsare selling at a large premium in the secondary market, this suggeststhat there will be high demand in the primary market. A mathematicalformula has been created that correlates secondary demand and primarydemand for tickets.

In addition to determining demand, this method also allows for adetermination of fair market pricing. Often tickets are listed for salein the primary market at prices well above those in the secondarymarket, such that the tickets in the primary market will not sell.Conversely, sometimes tickets are on sale in the primary market forprices well below those in the secondary market. The method describedallows for a precise understanding of the fair market price. This can beused to rationalize promotions or create pricing premiums so as tomaximize revenues for a given event.

The method can use either actual secondary ticket sales or it can uselisted tickets on the secondary market, even if they have not sold yet.If listed tickets are used, the data should be corrected for ticketsbeing listed at abnormally high or abnormally low prices. These ticketprices sometimes indicate factors personal to the sellers which shouldnot reasonably be aggregated to determine market supply/demand.

To correct for abnormally high or low prices on the secondary market inone embodiment, all data that is more than 1.5 interquartile ranges fromthe upper or lower quartile is rejected. The interquartile range is thedistance between the lower and upper quartiles. The remaining data isre-examined, and any additional data that falls outside of this limit isalso rejected. This analysis is repeated until all data falls within 1.5interquartile ranges. Other techniques can be used to eliminate unusuallistings.

In a preferred embodiment, the first step is to gather and assembleinformation about tickets and/or sales in the secondary market. This canbe done manually or can be accomplished using automated computersoftware, for example by so-called crawling software that retrievesinformation from one or more public websites accessed via the Internet.In addition, computer software can directly access private databases ofticket information if access has been arranged. Such databases caninteract with the automated computer software via plurality of meanssuch as SFTP (Secure File Transfer Protocol), direct SQL client-serverinterchange, etc.

FIG. 1 shows one example of a web page of a secondary market fortickets. Different events are listed, and the range of prices for eachevent are summarized for the user.

FIG. 2 shows an example of a web page accessible by clicking on one ofthe events listed in FIG. 1. A list of tickets, accompanied by sectionnumbers, is tabulated. The table has section column 276, row column 278,quantity of tickets for sale column 280 and price column 282. Byclicking on the respective ‘View Details’ link, more information can beobtained about the tickets for sale on the secondary market. Thisinformation can include the exact row and seat number of the seat(s)offered for sale, as well as the ticket ID or whether the seat(s) arelocated on an aisle.

If Internet sites are crawled, such as those with web pages similar toFIGS. 1 and 2, then analysis software in an embodiment first assembles aticket file in native Extensible Markup Language (hereinafter “xml”)code. This native xml file is then parsed by the software to extractrelevant variables such as Ticket ID, Section, Row, Seat (if available),quantity, price, and other special indicators such as whether or not theseat is an aisle seat etc. The software can also use rules coupled withthe known arena seating plan to determine the seat parameters.

The xml ticket information, which often contains the seating informationin free-form English which is not directly parsable, can be decoded forlater use. For example, a courtside seat may be annotated “CT”, “Court”,“Courtside”, “Floor”, etc. in the xml file. The software can have rulesand intelligence to uniquely decode these annotations. Depending on theamount of seats listed or other factors, either one or more Internetsites may be crawled for data.

Typically, many seats in an the arena stay constant from event to event,such as those in upper sections. Other seats may be arrangeddifferently, such as those on or near the floor around the basketballcourt. The software can use information from past seating arrangementsto determine the distance a seat is from the court, from an aisle, andother factors important to consumers. This distance and other factorsare then associated with the seat data from the xml file.

The software also relates the ticket price to the “face value” (or parvalue) of the ticket and the season ticket value of the ticket. Todetermine the season ticket value of a ticket, the price of the seasonticket can be divided by the number of games in the season. The facevalue and season ticket price can usually be obtained from public andprivate sources.

The parsed and decoded ticket data is then stored in a database,including annotations regarding the event ID, game, date, etc. The timeand date of the crawling and analysis can be included to allow forsubsequent analysis of ticket data over time.

The database used can be any one of a number of standard types, such asan ORACLE® database, Microsoft SQL Server® database, or other databasesknown in the art. The database can also be a mix of several databasesand/or database types as well. The use of a database helps store andorganize the data. Other methods of data storage besides databases arecontemplated, such as data files.

Once the ticket database is established it may be queried in a number ofways to extract relevant information. More complex queries are alsopossible, such as the ticket price history over time (e.g., 9 days).

A number of data can be extracted from the database. First, the pricehistory of the ticket over time can be established. If the price historyis changing, then the price history can be trended, and a prediction fora future date established. Second, the true selling price of the ticketcan be established as the price just before the ticket disappears fromthe database. Third, any rapid change of price (e.g., over a few days orhours) can be detected and an automated alarm brought to the attentionof an operator. This may indicate a loss of a key player or otherrelevant external events.

From each data point for a point in time for each seat ticket, adiscount or premium is calculated. A “premium” is a ratio of the pricefor a particular seat ticket in the secondary market over the face valueof the same ticket, preferably expressed as a percentage. A “discount”is simply a negative premium. An equation to determine thediscount/premium is:

premium=(price_(secondary) _(_) _(market)−price_(face) _(_)_(value))/price_(face) _(_) _(value))  (Eqn. 1)

where price_(secoodary) _(_) _(market) is the price on the secondarymarket and price_(face) _(_) _(value) is the price printed on thephysical ticket stub. A discount/premium can also apply to seasontickets with respect to the season ticket price as opposed to the facevalue.

The secondary market discount or premium can be used to determine thepricing of tickets. If an event is selling for a discount in thesecondary market, then new sales of tickets should also be at a discountto compete with the secondary pricing. The secondary market discount orpremium can be used to determine group pricing and individual pricingstrategies. An event trading at a secondary market discount may be soldto groups at a similar discount. An event trading at a premium in thesecondary market would suggest that a premium should be charged onoriginal ticket sales as well.

To aggregate the many different tickets for each event, the average ofall the premiums in the secondary market(s) can be calculated. Theresulting average premium can be associated with the event for furtheranalysis.

FIG. 3 is an example flowchart illustrating a process for determiningpricing for multiple similar events in accordance with an embodiment. Inoperation 302, a list of seat tickets and corresponding prices for saleon a secondary market for a future event are received. In optionaloperation 304, the list is filtered to remove outliers. In operation306, an average premium is determined for the future event based on thefiltered list of seat tickets and corresponding prices. In operation308, an average premium and a total revenue for each of multiple similarevents are received. In operation 310, the average premium and totalrevenue for each event of the multiple similar events are fit to amathematical model. In operation 312, revenue is determined for thefuture event based on the model and the average premium for the futureevent. By repetition of these operations, pricing for multiple similarevents can be determined.

As a further illustration, a plot of average secondary market premiumsversus total revenues can be made for multiple events, and therelationship between secondary market premium and the total revenues canbe established.

FIG. 4 illustrates the average secondary market premiums as compared tothe total revenues that were obtained for corresponding events. Thediscount/premium for each ticket at an event is averaged into an averagediscount/premium for the event, and the average discount/premium is thenplotted with total revenue for the event on an x, y seatter chart. Itcan be seen from the chart that the games with the largest secondarymarket discount (i.e., a negative premium) had the lowest total revenue.

Linc 454 is mathematically fit between the points so that futurerevenues can be predicted. For example, if secondary market salesindicate a discount of −10% from the face value of the tickets, then thetotal revenue can be predicted (from the straight line fit) to be$915,000.

In the figure, a linear equation is fit to the data, although it shouldbe apparent to one skilled in the art that curve fits could take someother functional form (e.g., polynomial). In this particular example,the linear correlation indicates that the total revenue of a futureevent can be calculated from the secondary market premium as totalrevenue=(secondary_market_premium*468.7)/0.0005011, where total revenueis in U.S. dollars and secondary_market_premium is expressed as apercentage. This correlation can then be applied to events in the futurewhere the total revenue is not known but is desired. For example, theresults of the first ten games in a season can be used to predict therevenues for the next fourteen games. The inventors have found inseveral analyses that agreement between the predicted and actualrevenues was within 10% and was better than 1% on average. Thisprediction can be repeated with time as an event approaches as thepremium or discount in the secondary market may change with time. Thecorrelation analysis described above used only the secondary marketpremium, but it could also use additional information like the day ofthe week of the event, the won/loss record of the team, or otherpertinent information. In certain aspects, the exact correlation willnot be the same for each team, performer, or act and will generally needto be determined separately for each case.

FIG. 5 shows such data plotted for an entire basketball season of eventsat an arena (i.e., home games). Some events had already occurred andother were still to be played. The left vertical dashed line indicatesthe minimum revenue that could be collected from each event. Thisminimum is determined from the apportioned revenue of season ticketsthat have already been sold. The right vertical dashed line in thefigure indicates the maximum revenue that could be collected in theprimary market from each event if each and every ticket were sold atface value. The secondary premiums and total revenues for the initialten games that had already been played were analyzed as described aboveand linear fit 586 was determined.

An event is predicted to sell out if the secondary market premium isgreater than the premium indicated by the intersection of the verticalsell out revenue line and linear fit 586 as determined by the premium tototal revenue relationship. Events 584 have a secondary demand thatsuggests that they should sell-out. The three data points correspondingto events 584 do not fall along the sell out vertical dashed line eitherbecause some tickets were subject to group discounts and other pricealterations or because the event had not been played and not all of therevenue had been generated. It should be noted, however, that withoutraising prices above face value, it will not ordinarily be possible togenerate the revenue that the secondary market predicts. The events witha premium below the intersection of the season ticket floor intersectionwith the same fit are indicative of those events that would not attainthe total revenue level without season ticket sales.

FIG. 6 shows the predicted revenue versus the secondary market premiumsas calculated using the equation of linear fit 586 of FIG. 5. Since theprojected revenue for future events can be calculated as soon as thereare tickets for sale in the secondary market, often months before anevent, such information can be used to determine prices and marketingstrategies for future events.

FIG. 6 also shows how events could be separated into different eventquality groups based on their projected total revenue. Thus, games canbe divided into different quality groupings based on their projectedrevenue (or premium or discount). Events with the highest quality (i.e.,highest potential revenues) will be those to the right of the rightvertical dashed line while those with the lowest quality (i.e., lowestpotential revenues) will be to the left of the left vertical dashedline. Events of intermediate quality will fall in between the verticaldashed lines. Using this technique, all events can be grouped into somenumber of quality bands where the number of quality groups is typicallybetween two and ten and more preferably between three and five.

Once the game quality or potential revenue for a series of games isknown, it is possible to group games into packages or to accuratelyprice the full season. High quality events can be bundled with lowerquality events and sold together in order to promote sales or lowerquality (i.e., lower demand) events. Specifically, a package or seasonticket may be determined to be fairly priced when the sum of thepremiums for the games is substantially near zero. For example, one cancombine an event trading at a 25% premium with one trading at a 25%discount so that the resulting package has a value equivalent to theoriginal ticket price. Of course, it may be desirable to offer acustomer a discount for committing to a package of multiple games so thesum of the premium for all games may be chosen to be slightly negative.As the number of the games in the package decreases, the discount (i.e.the sum of premiums) should approach zero.

Accordingly, secondary market data can be used to price tickets, or thedata can be used to bundle tickets. Both methods can be used together aswell to help increase revenue collected in the primary market.

(3) Pricing Sections, Rows, or Seats

Once a user has an idea of the proper price levels to be used fortickets (whether or not the user used the approaches described above),the user is then ready to create a price plan for the event.

An embodiment can be used to create a price plan. The embodiment caninclude downloaded or entered information about the venue in which theevent will be held.

Software can allow the user to assign prices to seats by creating acertain number of ‘price codes’ or price levels that relate to dollarvalues. The software extracts seat information from a venue map, theuser is prompted to assign price codes to sections, rows or seat blockswithin a row, and the dollar value for the price code is associated withthe specific location information. The dollar value of a price code canbe changed centrally, and such changes will update the price for everyseat identified as being part of that price code. The user can also seta price code to ‘kill’ (i.e., indicate that a particular location is notfor sale) while retaining its dollar value. For example, seats withobstructed views can be color coded or X′ed out so that it isimmediately clear to the user that the seats should not be listed forsale. The user may also indicate that certain seats are to be ‘held’(i.e., hold them from an initial sale period). The user can preview avisual representation of the pricing plan to make sure that thelocations by price and the overall financial potential of all tickets(if sold) meet the needs of those involved with the event.

The user can export the price plan's price codes and seat blockassignments to a spreadsheet, such as a Comma-Separated Variable (CSV)file, to be imported into a ticketing system. If the pricing system isdirectly linked to the ticketing system, then the pricing informationcan be directly submitted to the ticketing database. The user can createseveral ‘price plans’ for an event by assigning them different names andcan also share the plans via email or by printing the venue mapassociated with the different price plans.

An embodiment includes software that incorporates a seat level map forthe venue where an event will take place. This software includes adescriptor for each seat in the venue comprised of variables thatuniquely identify that location (e.g., section, row, and seat number).The software also includes a means for ascribing a price to each of theseats. The software integrates this information with seat map softwareand renders a drawing of the proposed seating map by price along withthe economics of the pricing for the venue or by price levels.

Another embodiment includes a system and method for correlating currentseat inventory information with the venue map. The inventory informationmay be uploaded or the system may have a direct connection to theinventory database. This allows the inventory to be displayed visuallyby status (e.g., available, sold, held, killed, inquiry), by price(e.g., the face value of the ticket for a particular seat), and by class(e.g., the type of hold, the type of package associated with the seat).

A database connected to or included in the embodiment can also includethe price for each specific ticket. The information from the databasecan be displayed on a venue map, along with other pertinent informationsuch as total possible revenue, total number of seats at a given price,or total revenue achieved and total remaining potential revenue.

When the display and visualization capabilities are used for planning orinventory control purposes, the seat status information or a data filecan be modified, either manually or through software that allows theuser to select seats on a seating chart. New information (e.g., priceinformation) about those seats can be input, thereby updating the datafile. This allows promoters, venue owners, or other stakeholders arelatively easy to use tool with which to try out different pricing andseating configurations before finalizing pricing for an event.

The display and mapping capability can be accessed through desktopsoftware or through a web-based application. A web-based application canallow users not linked to the ticket data to visualize ticketinformation. This may be particularly valuable to concert promoters thatwish to know about sales status but are not co-located with the ticketdatabase. A web-based system allows users to log in from a remotelocation, enter a password, and view data. In the case of a user thathas access to ticket data, this data is uploaded and the data from thefile can be viewed in a number of displays. For a user who does haveaccess to the ticket inventory database, the embodiment will present thelast data that was uploaded by someone with access. It is also possiblethat the system is connected to the inventory database and informationis updated automatically. There can also be different levels of accessfor different password holders, allowing each user access to apredetermined portion of the data.

FIG. 7 illustrates a web page for defining price levels in accordancewith an embodiment. Web page 100 displays price code table 110. Pricecode table 110 includes price code name column 102, price column 104,kill column 106, and edit link column 108. Price code table 110 includesfive price codes (price codes names “PL1” through “PL3K”). The ‘K’suffix on the end of a PL price level name indicates that the price codeis for killed seats. It may be desirable in some instances to create twoprice categories with different names but the same price. Once the pricecategories are determined, each section (or row or seat) is associatedwith a price category.

Cell 112 indicates the name of the second price code, PL2, andassociated cells 114 and 116 display the corresponding price and whetherthe seat has been killed, respectively. Cell 118 includes a link toanother web page to edit the name, price, or kill status of the pricelevel. A new price code can be added by clicking button 120

FIG. 8A illustrates a web page for correlating price levels of pricecodes with groups of seats in accordance with an embodiment. Price codetable 110 is reproduced on this web page, including cells 112 and 114showing the name and price for price code PL2. Another table on the webpage shows section column 222, seat count column 224, and price codecorrelation column 226. Using dropdown listbox 228, seats in Section 101of the venue, having a seat count of 394 seats, is assigned price levelPL2 (i.e., $85.00). Similarly using dropdown listbox 230, seats inSection 109 are assigned price level PL2K (i.e., $85.00 but not forsale). Changes can be submitted to the server by pressing button 232.

FIG. 8B illustrates a web page for correlating price levels with groupsof seats in accordance with an alternate embodiment. Price code table110 is shown with alternate price codes than those in FIG. 8A. Anothertable shows start section column 223 and end section column 225. Usingthese columns, a user can specify rules for the correlation of pricecodes or levels to particular sections. For example, section 101 throughsection 108 are assigned price code “P1,” and sections 109 through 114are assigned price code “P1” and can be selected as killed. New rulesfor assigning sections can be input by pressing button 233. Similar tothat for sections, rows and individual seats can also have rules forassigning price codes. For example, a rule that individual seats beyond±95° of the front of center stage are automatically killed can be inputby a user so that those seats are not sold.

Rules can also correspond to portions of the venue map. For example, auser might use a mouse to draw an electronic line arcing down betweensections 113 and 114, behind the mixing platform, and then betweensections 129 and 130 (see venue map at lower left of FIG. 8B). The usercould then designate that all sections behind this line be a certainreduced price. In a different embodiment, an performance artist coulduse a light pencil or signature pad to draw his or her own specialsymbol across a swath of seats on the venue map, so as to price theseats within the swath at a discount. This price discount method couldbe used to help market the event as well as build brand awareness forthe artist's special symbol. In another embodiment, the seats within theartist's symbol or mark could be priced at a premium instead of at areduced price. A concert-goer might be enticed to pay slightly more fora ticket to sit in the ‘point’ of the artist's exclamation point or thedot of her signature ‘i.’

FIG. 9 illustrates a venue map with correlated price levels from FIG.8A. Upon submission of the correlated price levels, venue map 900 isdisplayed on a separate web page. Venue map 900 shows the price levelscorrelated with seats. Legend 934 indicates to a user what price levelscorrespond to what graphics.

In certain instances, a price code can be a ‘flex price.’ A flex priceindicates that more than one price level is assigned to a price code.For example, a ‘flex up’ price and a ‘flex down’ price can be input sothat one or the other flex price can be selected during an on-saleevent, depending on demand or sales velocity.

For some ticket vendor software, such as Ticketmaster® ticket salessoftware, tickets cannot be re-priced during a sale, and new ticketscannot be added without stopping the sale to the public. For this andother reasons, it is sometimes helpful to assign multiple prices (e.g.,flex prices) for to the same seat before the sale. In this way, a usercan determine demand based on early sales data and then select onepre-entered price that best matches this demand without halting allsales.

FIG. 10A illustrates a web page for establishing a price code for flexcategories during pricing according to an embodiment. When editing aprice code, a user may check a flex checkbox in order to enable or makevisible textboxes for entry of flex prices. In the exemplary embodiment,two currency input textboxes are shown: one for a ‘flex up’ price, andone for a ‘flex down’ price. More than two input textboxes can be shownfor multiple flex levels. Flex up price level 1014 (i.e., $150) and flexdown price level 1015 (i.e., $100) are input by the user and stored in adatabase. A name indicating that the price code is a flex price code(e.g., “P1P2”, “P1Flex”) can be entered, although any name can beentered. After the price code is newly added or edited, the user submitsthe form using a submit or insert button.

FIG. 10B illustrates a web page for displaying all of the currentlyinput price codes for an event. Column 1002 of the table indicates thename of each price code. Column 1004 indicates the single price or theflex up price for each price code, and column 1005 indicates a flex downprice. A color, hatching, or other indicator can be assigned to eachprice level. FIG. 10B is similar to FIG. 7 but with a column showingflex down prices. Other columns (not shown) can display more than twoflex prices if applicable. More price codes can be added by clicking anew price button.

FIG. 10C illustrates another web page for displaying all of thecurrently input price codes for an event juxtaposed with a venue map.Table 1034 displays flex up and flex down prices. In addition, the tableshows the total value for seats in each price code. Price codes withdifferent flex up and flex down prices show different total values.Price codes with the same prices for flex up and flex down (i.e.,non-flex price codes) show the same total values in the flex up and flexdown total values.

The venue map in the figure shows different seat sections with hatchingindicating the corresponding price level. Other embodiments can showcolors or other indications of the price code down to the seat level.

FIG. 11 is an example flowchart illustrating a process in accordancewith one embodiment. In operation 1102, price levels are provided for anevent at a venue. In operation 1104, rules to attribute one or more ofthe price levels to the seats of the venue are received. In operation1106, each price level is correlated with the seats according to therules. In operation 1108, the price levels are displayed correlated withthe seats on a venue map. In operation 1110, a total projected revenuefor the event is calculated using the price levels. The number of seatscorrelated are also used to calculate the total projected revenue.

Price planning software can be used to visualize and edit pricingconveniently and intuitively. Electronic venue maps, displaying datadown to the seat level, can help promoters, venue representatives, andother stakeholders design price plans efficiently.

(4) Event Tracking

After the planning stage, the seats are set for sale. During the “onsale,” tickets can be bought up extremely quickly by fans or otherdistributors. The first few hours or even minutes of an on-sale can be adynamic confluence of pent-up demand and immediate supply.

A promoter, venue representative, or other stakeholder may wish to watchin real time the sale as it progresses. Some embodiments allow suchaccess on the same screens as indicated above.

Venue maps can allow a user to see all of the current inventory andimmediately distinguish by color, or gray shade, seats that are sold,those that are on hold, and those that are not for sale. In addition,hovering a mouse cursor or other electronic pointer over a seat graphicin the venue map can reveal additional information about the seatstatus, such as the type of hold or selling price of the seat.

Not only can one view information, but the visualization graphics canalso be used during the sale of tickets for inventory control purposes.In this instance, it may be beneficial to display additional informationalong with the venue map or diagram. Such information can include therevenue that a specific venue can generate given a current pricingstructure, the remaining revenue possible from unsold seats, and theamount of revenue that is not accessible due to seats on hold or thatare not for sale.

One embodiment includes a software system and method for storing andcomparing inventory status at different points in time. The systemallows the user to create a map of the inventory status at differenttimes or to display a map that compares the changes that have occurredbetween two or more points in time. This venue map can also be augmentedwith other graphical information about sales as a function of time,which can include the number of seats sold at each time interval intotal or by price, status, or class, the amount of revenue associatedwith tickets sold at each time interval in total or by price, status, orclass, or other time dependent information.

The embodiment can also save inventory information to a history page.Every time the user uploads a new inventory data file or the systemaccesses inventory data from a database, the system saves that data to ahistory file. The user has the ability to go to the history, select thefile, and build a map based upon the data in the file. The data from thehistory files can also be compared to each other. This can yield a newmap that displays inventory changes between the two files or can beshown in graphical form such as total revenue as a function of time.

The embodiment can integrate inventory information from the history fileto form a movie. This movie may be stored and played back as atime-lapse movie (or through high-speed photography techniques) foranalysis. By viewing how the seats fill, one can determine whether theseats fill ‘back to front’ or ‘front to back.’ If the seats fill frontto back, then higher priced seats in the front may not have high enoughprices in relation to lower priced seats in the back. Conversely, if theseats fill back to front, then higher priced seats in the front may havetoo high a price in relation to lower priced seats in the back. One canalso assess ‘side to middle’ and other geographical seating preferencesusing the updating. Effectively, the combined interests of individualticket purchasers are used to inform the viewer of the relative qualityof seats. Taking a snapshot of seat statuses at the beginning and endcan fail to give this type of insight.

Furthermore, watching the fill patterns of the venue's seating caninform a viewer of eccentricities of the venue. For example, if one sideof the venue fills faster than the other side, it could mean that thefirst side of the venue is more convenient to certain types oftransportation, bars and clubs, or other external influences. Also, suchfill patterns may indicate hidden consumer preferences with respect torestroom locations, concession stands, and air conditioning. This canavoid the expense and inaccuracies of customer surveys, focus groups,and other social data gathering techniques which are sometimesineffective.

FIG. 12A illustrates venue map 1200 with seat statuses during a sale oftickets for an event. Legend 1244 indicates seats that are sold, killed,held, or open. Venue map 1200 shows detail down to individual seats thatare sold. This map shows all seats based on their status (open, sold,held, killed) and shows the financial totals for each category (i.e., bysumming the number of seats sold at their respective price, the totaldollar value of the sold seats is calculated).

FIG. 12B illustrates an enlarged portion of region 1240 (section 125 andthe surrounding area of FIG. 12A). The status of each seat can bedisplayed. For example, seat 1242 is displayed as sold. Four rows back,a single seat in the row is still unsold (depicted in this case as acircle with a darker border but could also be based on fill color,shape, or other attribute). Several other single point unsold seats areshown in the section. The unsold seats can indicate problems in bundlingseats for sale. Determining in a section the density of single unsoldseats, which are less likely to sell than two or more seats, can help amarketing team estimate the overall density of single unsold seats forfuture events.

A venue map can also show other statuses of seats, such as whetherpotential buyers have inquired to purchase a seat.

FIG. 12C illustrates the venue map 1200 with seat statuses during a saleof tickets for an event. Table 1246 shows calculated revenue for theevent from the number and price levels of the sold seats. Legend 1248summarizes the number of held, open, and killed seats in the venue.

FIG. 12D illustrates a venue map which may be used to show the status ofseats which are reserved for management, the promoter, record label, andthe artist him or herself. Other types of holds may also exist and belabeled with specific or more general information. Color coding can beemployed so that more categories of stakeholders can be displayed in thelegend and on the venue map. While holds may be created before an eventgoes on sale, the status of holds can change during the course of anevent as seats are released for sale or new seats are held.

FIG. 13 shows an example flowchart illustrating a process in accordancewith another embodiment. In operation 1302, a first status of aninventory of seats of a venue at a first point in time is received. Inoperation 1304, the first status of the inventory of seats is displayedon a venue map. In operation 1306, a second status of the inventory ofseats at a second point in time is received. In operation 1308, thevenue map is updated with the second status of the inventory of seats.In operation 1310, the venue map is refreshed with additional statusesof the inventory of seats. Accordingly, a movie of the statuses is shownon the venue map. In operation 1312, the movie of the statuses on thevenue map is used to predict demand for a future event. For example,back-to-front buying of a past event can indicate that a future eventwill have similar back-to-front demand for seats. Pricing for the backseats can be raised accordingly to even out the demand.

Seat sales can also be visualized in other ways. For example, the numberof tickets sold can be plotted on line, bar, or similar charts withrespect to time. Charts are not limited to tickets sold but couldinvolve the number of inquiries, etc. Also, charts can be created forsubsets of tickets, such as tickets in a certain price level. The usercan also select the time frame over which the inventory data should beplotted. Using such charts, a user can determine if a radioadvertisement resulted in a bump up in ticket sales. Other factors canalso be correlated with ticket sales.

FIG. 14 is a plot of total tickets sold and inquiries with respect totime in accordance with one embodiment of the present invention. In anembodiment, sold tickets 1488 can be shown with inquiries for tickets1490 as shown. Other ticket statuses can also be plotted, such as thosefor held, open, and killed seats.

Watching ticket sales as they progress or plotting sales by time canhelp a promoter, venue representative, or other stakeholder determinemarketing techniques that are working and better estimate demand.However, one can also use the data to act upon the information in realtime to maximize revenue for an event.

(5) Changing Prices or Distribution Channel

As described above, it is preferred that flex price levels be selectedprior to an event.

Price codes can be assigned multiple price levels. For example, a pricecode for a group of seats can include a ‘flex up’ price level of $150and a ‘flex down’ price level of $120. The two price levels maycorrespond with prices in other, non-flex price codes. For example,price code “P1/P2” may have a flex up price level equal to that of pricecode “P1” (e.g., $150) and a flex down level equal to that of price code“P2” (e.g., $120). If one can predict demand for an event, one can usethe predicted demand to properly choose to ‘flex up’ or ‘flex down.’

During a sale of tickets, it has been found that the first few minutesof sales can be used to predict demand for later on in the same sale.Such real-time predictions may be based on a power model, exponentialgamma model, or other suitable models as will be described.

One embodiment includes systems and methods for determining ticketdemand during an on-sale event then allow a user to adjust salestechniques during the on-sale in response to the demand. This includessoftware that automatically predicts the number of tickets that willsell either in total or by price level. The software can then recommendto the user that certain tickets are moved from one status to another(i.e., flexed from a lower price level to a higher price level or viceversa). In some instances, the software will automatically re-priceinventory without user involvement. Because sales can move rapidly,sometimes automatic re-pricing is preferred.

The system can also be used for predicting the number of ticket salesfor single events, such as a concert on a specific date. Both long termsales (e.g., sales over a period of weeks ending in the event) and shortterm sales (e.g., minute by minute sales starting from the on-sale timeof the event) can be considered. Projecting total sales by price levelallows for seats to be priced at the appropriate level once demand isbetter understood. This flex pricing allows for tickets to be sold atthe highest possible price based on demand. The system can alsodetermine if the event will sell out, either in total or by pricecategory.

Several probabilistic models for the ticket sales modeling arecharacterized by having different hazard functions. The hazard functionis used to determine the duration dependence of ticket sales. Durationdependence is the relationship between time passing and an increased ordecreased likelihood of incremental purchases. Depending upon the hazardfunction, its shape can take several different forms.

Monotonic curves either decrease or increase throughout their duration.A monotonic curve in this application would suggest that the likelihoodof purchasing a ticket is either decreasing or increasing over time. Thedecreasing monotonic curve is appropriate for the short term on-salemodeling. Non-monotonic curves, although more complex, may beappropriate for long term modeling. A U-shaped hazard function can beused where there is an initial rush to secure seats at an event,followed by a lull in sales, and a second rush just before the eventdate.

A simple model for the sale of seats is the exponential model which isgiven by the equation:

S=A(1−e ^(−λ1))  (Eqn. 2)

where S is the cumulative ticket sales, A is a constant, λ is aconstant, and t is time from the start of sales. This model has aconstant hazard function of λ and asymptotes to the value of A.Parameters for this simple exponential model can be simply estimated byfitting a least squares straight line to a logarithmic plot of the data.

The simple exponential model's assumption of constant hazard rate is notrealistic for many situations. In sales of sporting event and othertickets, the initial sales rate rapidly drops. To accommodate this, apower model can be used, which is given by the following equation:

S=At ^(α)  (Eqn. 3)

where S is the cumulative ticket sales, A is a constant, t is the timefrom the start of sales, and a is a constant. This model has a hazardfunction which is linear. The function asymptotes to infinity.Parameters can be simply estimated by fitting a least squares straightline to a logarithmic plot of the data.

In FIG. 15 the actual number of tickets sold 1560 for an event isplotted with respect to time in comparison with a power model, the powermodel fit to two, three, and four data points. In this example, datapoints are available at 0, 9, 20, 34, 47, and 161 minutes after on-sale.In the exemplary case, two-point prediction 1562 and three-pointprediction 1564 extrapolate to lower ticket sales than there actuallyended up being; however, four-point prediction 1566, using the datapoints at 0, 9, 20, and 34 minutes, matches well to the actual number oftickets sold 1560 curve after 161 minutes.

FIG. 16 shows how the sales velocity or rate of ticket sales (i.e., thenumber of tickets sold per minute or delta per minute) can, for a powermodel, be estimated by straight line fitting on a logarithmic plot. Thelog-log plot of rate versus time offers an elegant way to predict therate of falloff in sales. This can be used to predict when the on-salecan be closed out, i.e., when the rate drops below x sales per minute.

The power model's asymptote at infinity is not altogether realistic, assales, particularly in the long term, will, of course, asymptote tototal seating capacity. Also the linear hazard function is not realisticfor long term sales.

The exponential gamma model allows for these characteristics; λ isdistributed across the population as a gamma distribution. Anexponential gamma model is given by the following equation:

S=A(1−(α/(a−t)^(α))  (Eqn. 4)

where S is the cumulative ticket sales, A is a constant, α is aconstant, a is constant, and t is the time from the start of sales.

The hazard function of the exponential gamma model has a decreasing formproportional to 1/t^(α). This is very suitable for events which exhibitnegative concave duration dependence, such as on-sales. Parameterestimation can be performed by using standard numerical optimizationsoftware. Maximizing the logarithm of the resulting likelihood functionhas been found to be helpful to obtain the maximum likelihood estimatesof the model parameters.

The Weibull-Gamma model is a generalization of the exponential gammamodel. The Weibull-Gamma model allows the hazard rate to vary. It is ofsimilar form to the exponential model (described above), except that tis replaced by t^(e) where c is a constant. The Weibull-Gamma model isgiven by the following equation:

S=A(1−c ^(−×t̂e))  (Eqn. 5)

where S is the cumulative ticket sales, A is a constant, λ is aconstant, t is the time from the start of sales, and c is a constant.

The hazard function of this model is very flexible and can be chosen byvarying parameters. Parameter estimation can be performed by usingstandard numerical optimization software. One can maximize the logarithmof the resulting likelihood function to obtain the maximum likelihoodestimates of the model parameters.

In addition to the single models described above, combinations of modelscan be used, provided enough data is available to accurately estimateparameters. Alternatively, different models can be used at differenttimes in the sale sequence. For example, one model can be used for theon-sale event, one model for long term sales, and another model after anintense marketing campaign.

Calculating how many seats to release at a given ‘flex’ price can bebased on current sales data and other parameters such as number ofinquiries. The relevant future point in time to predict is usually a fewhours into the on-sale. This time is called prediction time t_(p), whichis preferably set to 3 hours into the on-sale. If the model indicatesthat tickets sold P at time t_(p) will be greater than the number ofavailable open seats (O), then the number of flex seats to be releasedshould be equal to P−O (i.e., P minus O), if available. The predictionis adaptive in time. As data points are received, at a preferredgranularity of 5 minutes or faster, a new prediction is made, and theamount of flex seats to be released is updated.

A prediction can also be based on an adaptive exponential functionalmodel, which is given by the equation:

y(t)=A(t)(1−e ^(−λ(t)t))  (Eqn. 6)

where A(t) and λ(t) are parameters that are functions of time, and y(t)is the cumulative ticket sales at time t. Hence the prediction of ticketsold P is given by using the currently estimated parameters, and settingt=t_(p). The hazard function of this model is A(t)*λ(t), and as theseare adaptively modeled in time, almost any shape of hazard function canbe modeled. This is, therefore, a generalization of the previouslydescribed exponential-gamma and Weibull-Gamma modeling methods. Inparticular the characteristic ticket sales rate that occurs during anon-sale in which λ (the ticket sales rate) initially rapidly increasesfor 10-20 minutes then decays, is readily modeled.

The prediction algorithm proceeds as follows. Two data points should beavailable before the above parameters are estimated and the predictionbegins. It is preferable that the data points be at 5 minutes and 10minutes into on-sale so that ticket sales have stabilized. However, itcan be important that the algorithm begin predicting as soon aspossible. It is possible to implement various tests of statisticalconfidence to ensure that the initial two data points used have stablestatistics. Therefore, data points may come in, for example, as fast as1 minute apart, and the algorithm can automatically choose which pointsto use as the initial two-points based on calculated statisticalconfidence.

Best fit may be determined by a simulated annealing algorithm. In asimulated annealing algorithm, parameters A and λ are moved in verysmall increments either up or down from their current values, and if theadaptive exponential function with the new parameters better fits thedata points, then A and/or λ are moved to the new values. This processis repeated.

At each point in time, a new A and λ are calculated from actual datapoints. After all the data points are collected, the different A's andλ's are preserved as A(t) and λ(t). These functions of time can be usedto predict future events.

In one example, data points y₁, and y_(i) where y₁ is the first datapoint at 5 minutes, and y_(i) is the current data point (at 10 orgreater minutes). For point 1, the following initialization can beperformed:

A=y ₁*ln(PredictTime)/ln(t _(i))

Lambda=1 SumPredict=0 MeanPredict=0

alpha=0.01

Anneallterations=1000 TotalReleasedFlex=0

FOR points i>=2 do the following:yEst₁=A*(1−exp(−Lambda*t₁yEst_(i)=A*(1−exp(−Lambda*t_(i)))RMSerror=sqrt(((y₁−yEst₁)̂2+(y_(i)−yEst_(i))̂2)/2)

A stochastic simulated annealing algorithm can be implemented toestimate the parameters A and Lambda for the current data point, asfollows:

FOR 1 to AnnealIterations DO  ′vary A first by plus or minus a verysmall number (alpha)  IF RND( 1 ) > 0.5 THEN sign = 1 ELSE sign = -1 NewA = A * (1 + sign * alpha)  ′calculate yEst₁, yEst_(i), andNewRMSerror using NewA  yEst₁ = NewA * (1 - exp( -Lambda * t₁)) yEst_(i) = NewA * (1 - exp( -Lambda * t_(i)))  NewRMSerror = sqrt( ((y₁ - yEst₁){circumflex over ( )}2 + (y_(i) - yEst_(i)){circumflex over( )}2) / 2 )  ′use NewRMSerror if a better fit than the previousRMSerror  IF NewRMSerror < RMSerror THEN  ′accept the NewA as better  A=NewA   RMSerror = NewRMSerror  END IF  ′vary Lambda by plus or minusa very small number (alpha)  IF RND(1) > 0.5 THEN sign = 1 ELSE sign =-1  NewLambda = Lambda * (1 + sign * alpha)  ′calculate yEst₁, yEst_(i),and NewRMSerror using NewLambda  yEst₁ = A * (1 - exp(-NewLambda * t₁)) yEst_(i) = A * (1 - exp(-NewLambda * t_(i)))  NewRMSerror = sqrt( ( (y₁ - yEst₁){circumflex over ( )}2 + ( y_(i) - yEst_(i)){circumflex over( )}2) / 2 )  ′use NewRMSerror if a better fit than the previousRMSerror  IF NewRMSerror < RMSerror THEN  ′accept the NewLambda  asbetter  Lambda = NewLambda  RMSerror = NewRMSerror  END IF END FOR  ′endof the annealing loop

When the annealing loop finishes the parameters A and Lambda areestimated and can be used to form the current prediction. The predictionis calculated by the following:

yPredict_(i) = INT( A * ( 1 - exp( -Lambda * PredictTime) ) ) ′this isthe current point prediction SumPredict = SumPredict + yPredict_(i)MeanPredict = INT(SumPredict / ( i - 1 ) ) ′ mean from point 2 to thecurrent point ′Perform a correction on the mean prediction as follows:IF MeanPredict < y_(i) THEN  ′prediction should not be less than currentdata point  IF yPredict_(i) > y_(i) THEN   MeanPredict = yPredict_(i)  ′use the current prediction instead of the mean  ELSE   MeanPredict =y_(i)      ′set equal to current data  END IF END IF

MeanPredict is then used to release flex seats:

CurrentReleaseFlex = ( MeanPredict - StartOpen ) - TotalReleasedFlex IFCurrentReleaseFlex < 0 THEN CurrentReleaseFlex = 0 ′cannot un-releaseseats IF ( CurrentReleaseFlex + TotalReleasedFlex ) > StartFlex THEN CurrentReleaseFlex = StartFlex - TotalReleasedFlex ′cannot release > remaining END IF TotalReleasedFlex = TotalReleasedFlex +CurrentReleaseFlex

One can also use the number of inquiries to improve the predictionalgorithm. An inquiry (or enquiry) is a ticket or set of tickets that iscurrently being held for a short amount of time (e.g., 2 minutes) whilea potential customer decides to buy or not to buy. An inquiry can end ifa user declines to buy tickets, if the user buys the ticket, or upontimeout of the hold time. Inquiries are therefore a predictor of salesfor a short time (e.g., 2-10 minutes) into the future. By using aprediction of the number of sales within a short time, Δt, in the futureby using the inquiries, one can improve the prediction model previouslydescribed.

A prediction of the number of tickets to be sold within a short time inthe future can be modeled by the following linear model:

y _(t+Δt) =y _(t)+β_(t) E _(t) Δt  (Eqn. 7)

where Y_(t+Δt) is an estimate of the number of tickets sold at a pointt+Δt, y_(t) is the number of tickets currently sold at time t, β_(t) isa parameter that is estimated from the data, E_(t) is the current numberof inquiries, and Δt is a small period of time.

The prediction y_(t+Δt) can be used in the previous algorithm asfollows. Instead of using two points to estimate the parameters A andLambda, one uses three points: y_(t+Δt), y_(i), and y₁. Thus, theprevious annealing algorithm will compute and minimize 3 component errorestimates, i.e.

NewRMSerror=sqrt(((y ₁ −yEst₁)̂2+(y _(i) −yEst_(i))̂2+(y _(i+1)−yEst_(i+i)))/3)

where y_(i+1)=y_(1+Δt).

The parameter β_(i) is estimated as follows.

β_(i)=(y _(i+1) −y _(i))/((t _(i+1) −t _(i))*E _(i))=(dy/dt _(i))/E _(i)

That is, β_(i) is the rate of ticket sales divided by the number ofinquiries. One then can make the estimate that β_(i+1)=β_(i). Then forany point i, the future point y_(i+1)=y_(t+Δt) can be estimated fromcurrent and past data as follows:

y _(i+1) =y _(i)+((y _(i) −y _(i−1))/(t _(i) −t _(i−1)))*(E _(i) /E_(i−1)))Δt

where Δt is a small time period between the current point and the futurepoint.

Note that as dy/dt_(i)=(y_(i)−y_(i−1))/(t_(i)−t_(i−1)), one isessentially modeling the future rate of ticket sales as the current ratemodified by the factor E_(i)/E_(i−1), which is an indicator of the trendof the level of inquiries. In this way, the number of inquiries can beused to augment the existing data points with a data point slightly inthe future. The extra data point helps in determining the properparameters for one of the above models.

FIG. 17 is a flowchart with operations in accordance with an embodiment.In operation 1702, a rate at which a first inventory of seats have soldfor an event at a venue is determined. In operation 1704, a demand iscalculated for a second inventory of seats based on a hazard functionalgorithm which uses the rate at which the first inventory of seatssold, the seats of the first and second inventories being comparable inquality. In operation 1706, one of a plurality of price levels at whichto release the second inventory of seat is determined using the demand.

One or more of the models above can be used to predict the number ofsales for an event while a sale of tickets is in progress. Using thatprediction, one can determine whether to flex up or flex down.

FIG. 18 is a plot of tickets sold on the one-hundred level of a stadiumwith respect to time in accordance with one embodiment of the presentinvention. Also plotted are horizontal lines delineating the predictednumber of seats to be sold 1894, the number of open seats 1896, and thenumber of open seat and flex seats 1898. A user can view the number ofseats sold in time (e.g., pseudo-real time) and determine, based on thepredictions and hard lines, at what time and what price to release theflex seats. The prediction line can be updated with the predictionalgorithms described above.

Inquires can be used in a different way to estimate the total sales foran event. The inventors have determined that there is a strongcorrelation between the trend of the ratio of tickets sold to inquiresand the overall number of tickets that will be sold in an on-sale event.

FIG. 19 is a plot of tickets sold versus number of inquires at differenttimes for a particular event that can be used in accordance with oneembodiment of the present invention. As shown in the figure, the numberof inquiries can be matched to the number of tickets that are sold at agiven point in time. Data points 1902 are fit to a linear model 1904 orexponential model 1906 and interpolated or extrapolated as desired.Using only a few number of inquiry/tickets sold data points at thebeginning of on-sale (e.g., the rightmost three data points in thefigure), the model can be used to form an independent estimate of thetickets sold at the end of the on-sale. The intercept of the modelfunction with the y-axis (vertical axis) gives the estimate. For linearmodel 1904, this estimate is 619 tickets; the actual number of ticketssold at the end of the on-sale for this event was 657 tickets.

In a preferred embodiment, the model is fitted point by point as datacomes in. In an actual on-sale it is possible that the number ofinquires increases initially before diminishing. This results in theintercept of the model line not crossing the y-axis in a positivelocation. To correct for this modeling problem, the algorithm can waituntil the number of inquires decreases, and then declare this point the‘start point.’

A least squares straight line fit can be fit to the inquiry data.Alternatively, a least squares straight line fit can be fit to thenatural log of the data in order to determine an exponential fit.

The linear model, the exponential model, or both can be used to estimatefinal ticket sales. To use both, the prediction from the linear modeland exponential model can be averaged together. An alternative method isto weight the linear model and exponential model differently, where theweightings are determined from the data as it comes in point by point.

The estimate of the final on-sale sales using inquiries can be usedindependently or in conjunction with the previously disclosed methodsfor estimating final sales.

The number of seats that are recommended for release can be correlatedto specific regions of the venue so that tickets are flexed in a uniformmanner. For instance, it may be desirable to flex an entire row at onetime rather than flexing up the price of some seats in a row whileothers are left at a lower price. The flex recommendation can beintegrated with the venue configuration information from the maps.

In addition to flexing entire rows, it may be desirable to flex seats by‘x-number.’ An x-number is a feature that some ticketing software usesto further classify the seats in the venue. Typically, x-numbersidentify which seats are presumed better than other seats. An x-numberdoes not need to include an entire section but can be a sub-section;however, an x-number does not normally include seats in more than onesection. X-numbers are assigned to sections or portions of sections andcan be utilized to prioritize the sale of tickets. It may be desirableto flex seats within one x-number, and this information may also becontained in the venue configuration stored within the system.

In addition to the total additional revenue that an event can generate,additional factors may be important in prioritizing sales for flexpricing. One such factor is sales momentum, such as a sell out, whichmay provide potential customers with the indication that the tickets arein limited supply and may also increase the excitement associated withthe event. Also, the remaining inventory may be considered along withthe likelihood that a game will sell out (i.e., the momentum aspect offocus). In practice, group sales usually drop off two to four weeksprior to the event while single event tickets pick up as the eventapproaches. Historical trends regarding single and group event ticketscan be factored in to provide a more accurate indication of additionalforecast ticket sales. In certain aspects, this information can becombined with the total revenue forecasts to create a clear picture oflikely sell outs. If a sell out is likely, then prices can be flexedaccordingly to maximize revenue.

(6) Posting Inventory to the Secondary Market

In addition to or instead of changing or flexing the price of inventoryin the primary market, it may be desirable for a seller in the primarymarket to sell some of the inventory directly on the secondary market.For instance, tickets in the first few rows of floor seats at a concertmay sell for a higher price on the secondary market than the originalface value of the ticket. This may also be true of the first rows, evenin the second or third level of a venue.

To capitalize on the higher prices, key questions the primary ticketseller encounters are: (1) how many and which tickets should be heldfrom the primary market and released to the secondary market, and (2) atwhat price should the tickets be released?

The present invention provides algorithms which can recommend answers tothe above questions of “how many tickets should be released?” and “atwhat price?” The algorithms ensure a high probability that these ticketswill be among the “best” value of all secondary tickets. Because therecommended tickets will be of greater value than other tickets listedon the secondary markets for the same event, it is probable that therecommended tickets will actually be sold and realize the potentialextra revenue.

The first step in the algorithm is to produce a list of the tickets onthe secondary market(s) by equivalent or comparable section for theevent. This utilizes the crawling and analysis software previouslydescribed.

Referring back to FIG. 9, comparable sections of seats include thosewith similar viewing quality for the event. For the exemplaryconfiguration in FIG. 9, sections 115 and 128 are comparable to eachother in that they are each an equal distance from the stage. Likewise,sections 117 and 126 are also comparable to each other. However,sections 117/126 would typically be considered as having a higherviewing quality than sections 115/128 because sections 117/126 arecloser to the stage. For sporting events in which the entire field areaof the stadium is the viewing area of interest, section 115, 117, 128,and 126 would all be comparable to each other in that they are an equaldistance from the center point of the field.

Once equivalent sections are defined (if desired) secondary listings forthe event in question are aggregated by equivalent section. The data foreach equivalent section is filtered.

Seat quality can also be objectively determined at the row or seatlevel. For example, one can create a scalar seat quality which takesinto account the distance of the seat from the front of the stage, theheight of the seat above the floor, the distance to an aisle, thedirection of the stage from a straight out view from the seat, and thedeviation from the centerline of the stadium. One can adjust weightingfactors or other linear or non-linear parameters to better align withconsumer preferences from actual data as discussed below.

However, objective determinations of seat quality do not always matchsubjective determinations of seat quality. A myriad of consumerpreferences besides those outlined above often figure into one'sdetermination of a value of a seat. Furthermore, just how much theobjective determinations of seat quality affect one's preferencesremains unknown. For example, a row closer to the field may be desired,but whether one row closer is worth $1, $5, or $50 more is difficult toestablish. The algorithm compensates for many of these unknowns by usingempirical data from the secondary market.

The second step in the algorithm is to model the relationship betweensecondary premium and row number. In general, the premium is highest forrows at the front of a section and decreases with increasing row number.This relationship is modeled using the functional form:

Premium_(row) =A*1/(Row^(alpha))  (Eqn. 8)

with Premium_(row) being the discount/premium calculated above and Rowbeing the row number of the seat. The parameters A and alpha areextracted by using a best least squares fit to the data. Note that ifalpha turns out to be positive, this means that the price is increasingwith increasing row. In this case the model is rejected and noprediction can be made for this equivalent section.

The algorithm then uses the model to determine the price of the ticket,given that it is desired to achieve a certain minimum premium(Premium_(desired)) above face value. For example, this desired premiumcan be chosen as “Aggressive,” “Moderate,” or “Conservative” and set toa certain value, for instance Premium_(desired)=2.5, 2.0, or 1.5respectively, indicating a price of 3.5, 3.0, or 2.5 times face valuerespectively.

Given one or more desired premiums, the algorithm starts at row 1 in asection and computes Premium_(row) for each row using the above model.If Premium_(mw)>=Premium_(desired), then it is concluded that ticketscan be released from hold for this row at price (1+Premium_(row))*Face,and have a high probability of actually selling in the secondary market.This is because these tickets will be better than or equal to the bestvalue tickets currently on the secondary market. The result is adetermination of how many rows of tickets could be placed in thesecondary market for each equivalent price section given the desiredminimum premium.

Because ticket data in the “best value” list may be sparse, the modelPremium, may have large errors. For example, if only two tickets in thesecondary market were at rows 15 and 20, the predicted premium for row 1may be well off. In order to handle this, the algorithm also computes a“Data Quality” metric, QEQ, for each equivalent section. If this metricis below a threshold, then the data is rejected and no predictions aremade for this comparable section. One preferred metric is given by:

QEQ=1−(Row_(minimum)/Row_(maximum))  (Eqn. 9)

where Row_(minimum) is the row number of the lowest row in thecomparable section list and Row_(maximum) is the row number of thehighest row. This metric lies between 0 and 1 and has a small value ifthe minimum and maximum rows in the data are close together or far fromRow 1.

While the calculations above define the number of rows of tickets, andtherefore the total number of seats, that could be placed on thesecondary market, it is desirable to release or post the tickets to thesecondary market over time so as to not create more supply than demand.

The number of tickets to be released can be computed in various ways.For example, a certain number of tickets can be released at a fixed timeinterval (for instance every hour). It is preferable to release ticketsdepending on the number of tickets currently on the secondary markets soas not to depress prices. For example, the number of tickets that can bereleased can be determined by taking a fixed percentage of currentlistings. Ideally this number would be less than 50% and more likelyless than 20% of the existing secondary listings. This calculation canbe repeated as the listed seats sell, always maintaining a level ofseats lower than some percentage of the total secondary listings. Thismethod could also be further refined by considering the exact locationwithin the venue and maintaining the number of listed tickets below acertain percentage of like tickets.

FIG. 20 shows an example of the algorithm's final recommendations to auser in summary form. Recommendations are shown for floor seating andfixed section seating along with potential secondary revenue for eachseating area. The total potential secondary revenue is also shown.

FIGS. 21A and 21B show example outputs from the algorithm. Tabulated areaggressive (“A”), moderate (“M”), and conservative (“C”) pricing andrevenue potentials. The recommendation sections at the bottom of eachfigure are for different levels of “Aggressive Pricing.” Also shown isthe total potential extra revenue for each scenario. The recommendationsections for all the seating in the venue can be summarized in a webpage such as that shown in FIG. 20.

In one embodiment, a system and method for determining the demand andpricing of inventory in a secondary market is provided. Software usesthis information to determine if inventory should be removed from theavailable primary inventory to be sold on the secondary market. Thesoftware can either make suggestions to the user or make changesautomatically without user involvement. This analysis can provide aspecific value for each seat in the venue. The software can thenrecommend how many seats in each section should be removed frominventory and sold in the secondary market.

The analysis of on-sale demand described above in regard to modifyingprimary ticket pricing during the sale can predict if the event or ifprice sections will sell out. This same information can be used todetermine how many and at which price secondary inventory should bereleased. For instance, if the analysis indicates that a certain pricelevel will quickly sell out, then more inventory from these sectionscould be placed on the secondary market at a higher price. In this way,the calculation of on-sale demand can be coupled with the secondarymarket analysis to automatically determine how aggressively thesecondary market should be utilized.

Software can combine ticket information from the primary and secondarymarkets and display all inventory in a single map. In this way, thetotal revenue from an event, whether derived from primary sales or fromby selling inventory in the secondary market, can be tracked.

(7) Improved Display of Secondary Inventory for Sale

Not only can data from secondary markets be used to project totalrevenue for an event, the data can also be used at the section, row, orseat level to determine the market value of individual seats. The marketvalue can be used to display a ‘best value’ seat or selection of seatsto a user. This allows a user, who may not have the time or expertise todiscern the benefits of a set of seats, to have a more independent basisfor selecting good seats.

FIG. 22A shows ticket premiums gathered and calculated from thesecondary market plotted with respect to row number for a particularsection in an arena. The data is fitted to linear model 2208. This modelcan be used to indicate to a customer where the ‘best value’ seat in thesection is located. Note in the figure that tickets near the front(e.g., rows 2-4) are valued in the secondary market as 250%-300% overthe season ticket price while tickets near the back (e.g., rows 13-14)are valued at less than half the premium (i.e., 50-125%) of the frontseats.

In certain aspects, the deviation of the seat price from the model isdetermined in order to ascertain the best valued ticket available on thesecondary market. For example, the fifth ticket 2206 in FIG. 22B is inrow 6. From the linear model (i.e.,Premium_(predicted)=−0.1814*row_number+3.2893), the predicted premium is2.2009 (220%). In contrast, the ticket premium in the secondary marketis 141%. The ticket value, which is the predicted premium minus thepremium in the secondary market, is thus 220%−141%=79% (0.79). The bestvalue ticket will have the highest ticket value and the lowest actualpremium relative to that predicted by the fit for all tickets. Thiscorresponds to the ticket that falls the farthest below the line in FIG.22A. By comparing the ticket value for all tickets in this manner, theticket with the best value when row number is considered is determined.Combining the results of this analysis with the analysis above that didnot consider row number, two tickets for the section that represent thelowest price and the best value are determined. That is, the lowestprice is the ticket on row 14, and the best value ticket is the one onrow 6 (calculated above). Both of these tickets are stored forpresentation to a purchaser.

It is also possible to determine relative value by first finding thelowest priced seat for the lowest listed row within a section orequivalent section. This listing is stored for future display. The nextlowest row with a price lower than the stored listing is also stored.This process is repeated until the last row with a listing for thesection or equivalent section is analyzed. The list of stored seats orlistings can then be presented to a potential purchaser.

FIG. 26 displays venue map 2600 for presentation to a purchaser inaccordance with an embodiment. The map displays the rows having thefifty best value tickets in the arena, according to the calculationsabove based on secondary market premiums. The rows are color coded orhatched according to price level of the tickets. Other methods ofhighlighting the seats can also be used. A user can hover an electroniccursor (e.g., a mouse pointer) over one of the highlighted rows so thata tooltip pop-up window appears. The pop-up window displays details ofthe best value ticket in the row, such as how many tickets are availableand for what price. A user can then click or depress a button whilehovering over the row in order to initiate a purchase. Also displayedare rows having seats which are not the best value according to thecalculations are also shown on the venue map as well as an overall countof the number of secondary market tickets found. This can give the usera sense of how many tickets are out there and what percentile the bestvalue tickets are.

The venue map can be produced by software that takes in the locations ofseats from accurate blueprints or drawings of the arena seating andproduces a Scalable Vector Graphics (.svg) format file. An .svg file canallow a user to zoom in to the smallest of details in the graphics filedepicting a seat. Zooming in can allow the user to judge such things asthe width, direction, whether there is a cup holder, and other personalconsiderations, if any.

Additional data can be presented to help the purchaser with his or herselection. For example, the average price or premium for tickets in aspecific section or at a particular price point can be provided. Thiscan allow the customer to see the price or premium of the selected seatrelative to the price or premium of other similar seats.

It is possible to further reduce the number of choices that a purchaserhas by comparing the optimal or best value seats to preferences that thepurchaser has provided. One such preference is the number of seats thatthe customer wishes to purchase. In this aspect, any ticket listing witha number of seats below the required number is eliminated, therebyreducing the number of choices. Some other possible preferences arepreferences in cost, value, or closeness to the event and how much thepurchaser is willing to pay.

The user's preferences in terms of price, value, and location can beincorporated into the selection of seats to offer the user by computinga distance metric which gives an indication of the closeness of theparticular seat offering to the user's stated preferences. This metriccan range from 1 (exactly matches the users preference) to 0 (does notmatch at all). A threshold can then be put on the metric to offer onlyseats above the threshold.

In some instances, there are several sections in an arena at the sameoriginal ticket price. Referring to FIG. 26, sections 101, 102, 119,110, 111, and 112 all have the same original face value ticket price. Insome cases, the value of one section may provide enhanced value relativeto other sections. For instance, sections 101 and 111 may be consideredsuperior sections since they are more centrally located to the court. Inthis case, the relative value can be determined as described above usingonly row number or the premium can be determined using a least squaredanalysis where row and section are treated as variables. In the lattercase, the sections can be assigned a separate value based on theirlocation. In this case, sections 101 and 111 are assigned one value andsections 102, 110, 112, and 119 are assigned a second value. The fit ofpremium to row and section can then be calculated.

If no original pricing information is known, the aggregated ticketprices can be compared on a section by section basis because it wouldnot be possible to determine pricing on an original price basis. Thismay result in more listings than would be provided if the originalticket values were known but relative value of seats within a sectioncan still be determined.

FIG. 23 shows a plot of premiums for seat tickets versus row numbers. Itis apparent from the chart that there is a noticeable correlationbetween the row number and seat price for this example, even though theseats are separated from each other by mere tens of feet.

FIG. 24A and FIG. 24B show one analysis of prices and demand in thesecondary market prior to the on-sale of that event. In this embodiment,this analysis determines the price as a function of row for seats nearthe stage of a concert. In FIG. 24A, data is analyzed for floor seatsfor the two sections at the left and right of the center section. FIG.24B shows the same analysis for the floor seats in the center section.The secondary prices for seats in the same row are higher for the centersection than for the left and right sections.

Although it is common knowledge that center sections are generallypreferred to side sections, it was previously not known how much morecenter sections are worth than side sections. This analysis based onsecondary markets gives concrete estimations of that difference inworth, down to the individual row.

FIG. 25 is a flowchart with operations in accordance with an embodiment.In operation 2502, a list of seat tickets and corresponding prices forsale on a secondary market are received. In operation 2504, the seattickets in the list are grouped by comparable sections of seats. Inoperation 2506, the prices of one of the groups of seat tickets are fitas a function of row to a mathematical model. In operation 2508, a priceis calculated for seats in a row from the mathematical model. Inoperation 2510, an inventory of unsold seat tickets on the row isreceived. The inventory of unsold seat tickets have a predeterminedprice. In operation 2512, it is determined whether the inventory ofunsold seat tickets is priced lower than the calculated price. Inoperation 2514, the inventory of unsold seat tickets is filtered basedon the determination of whether the inventory of unsold seats is pricedlower than the calculated price. In operation 2516, the filteredinventory is then displayed.

FIG. 27 is a block diagram illustrating components of an exemplaryoperating environment in which various embodiments of the presentinvention may be implemented. The system 2700 can include one or moreuser computers, computing devices, or processing devices 2712, 2714,2716, 2718, which can be used to operate a client, such as a dedicatedapplication, web browser, etc. The user computers 2712, 2714, 2716, 2718can be general purpose personal computers (including, merely by way ofexample, personal computers and/or laptop computers running a standardoperating system), cell phones or PDAs (running mobile software andbeing Internet, e-mail, SMS, Blackberry, or other communication protocolenabled), and/or workstation computers running any of a variety ofcommercially-available UNIX or UNIX-like operating systems (includingwithout limitation, the variety of GNU/Linux operating systems). Theseuser computers 2712, 2714, 2716, 2718 may also have any of a variety ofapplications, including one or more development systems, database clientand/or server applications, and Web browser applications. Alternatively,the user computers 2712, 2714, 2716, 2718 may be any other electronicdevice, such as a thin-client computer, Internet-enabled gaming system,and/or personal messaging device, capable of communicating via a network(e.g., the network 2710 described below) and/or displaying andnavigating Web pages or other types of electronic documents. Althoughthe exemplary system 2700 is shown with four user computers, any numberof user computers may be supported.

In most embodiments, the system 2700 includes some type of network 2710.The network may can be any type of network familiar to those skilled inthe art that can support data communications using any of a variety ofcommercially-available protocols, including without limitation TCP/IP,SNA, IPX, AppleTalk, and the like. Merely by way of example, the network2710 can be a local area network (“LAN”), such as an Ethernet network, aToken-Ring network and/or the like; a wide-area network; a virtualnetwork, including without limitation a virtual private network (“VPN”);the Internet; an intranet; an extranet; a public switched telephonenetwork (“PSTN”); an infra-red network; a wireless network (e.g., anetwork operating under any of the IEEE 802.11 suite of protocols, GRPS,GSM, UMTS, EDGE, 2G, 2.5G, 3G, 4G, Wimax, WiFi, CDMA 2000, WCDMA, theBluetooth protocol known in the art, and/or any other wirelessprotocol); and/or any combination of these and/or other networks.

The system may also include one or more server computers 2702, 2704,2706 which can be general purpose computers, specialized servercomputers (including, merely by way of example, PC servers, UNIXservers, mid-range servers, mainframe computers rack-mounted servers,etc.), server farms, server clusters, or any other appropriatearrangement and/or combination. One or more of the servers (e.g., 2706)may be dedicated to running applications, such as a businessapplication, a Web server, application server, etc. Such servers may beused to process requests from user computers 2712, 2714, 2716, 2718. Theapplications can also include any number of applications for controllingaccess to resources of the servers 2702, 2704, 2706.

The Web server can be running an operating system including any of thosediscussed above, as well as any commercially-available server operatingsystems. The Web server can also run any of a variety of serverapplications and/or mid-tier applications, including HTTP servers, FTPservers, CGI servers, database servers, Java servers, businessapplications, and the like. The server(s) also may be one or morecomputers which can be capable of executing programs or scripts inresponse to the user computers 2712, 2714, 2716, 2718. As one example, aserver may execute one or more Web applications. The Web application maybe implemented as one or more scripts or programs written in anyprogramming language, such as Java®, C, C# or C++, and/or any scriptinglanguage, such as Pert, Python, or TCL, as well as combinations of anyprogramming/scripting languages. The server(s) may also include databaseservers, including without limitation those commercially available fromOracle®, Microsoft®, Sybase®, IBM® and the like, which can processrequests from database clients running on a user computer 2712, 2714,2716, 2718.

The system 2700 may also include one or more databases 2720. Thedatabase(s) 2720 may reside in a variety of locations. By way ofexample, a database 2720 may reside on a storage medium local to (and/orresident in) one or more of the computers 2702, 2704, 2706, 2712, 2714,2716, 2718. Alternatively, it may be remote from any or all of thecomputers 2702, 2704, 2706, 2712, 2714, 2716, 2718, and/or incommunication (e.g., via the network 2710) with one or more of these. Ina particular set of embodiments, the database 2720 may reside in astorage-area network (“SAN”) familiar to those skilled in the art.Similarly, any necessary files for performing the functions attributedto the computers 2702, 2704, 2706, 2712, 2714, 2716, 2718 may be storedlocally on the respective computer and/or remotely, as appropriate. Inone set of embodiments, the database 2720 may be a relational database,such as Oracle 10g, that is adapted to store, update, and retrieve datain response to SQL-formatted commands.

FIG. 28 illustrates an exemplary computer system 2800, in which variousembodiments of the present invention may be implemented. The system 2800may be used to implement any of the computer systems described above.The computer system 2800 is shown comprising hardware elements that maybe electrically coupled via a bus 2824. The hardware elements mayinclude one or more central processing units (CPUs) 2802, one or moreinput devices 2804 (e.g., a mouse, a keyboard, etc.), and one or moreoutput devices 2806 (e.g., a display device, a printer, etc.). Thecomputer system 2800 may also include one or more storage devices 2808.By way of example, the storage device(s) 2808 can include devices suchas disk drives, optical storage devices, solid-state storage device suchas a random access memory (“RAM”) and/or a read-only memory (“ROM”),which can be programmable, flash-updateable and/or the like.

The computer system 2800 may additionally include a computer-readablestorage media reader 2812, a communications system 2814 (e.g., a modem,a network card (wireless or wired), an infra-red communication device,etc.), and working memory 2818, which may include RAM and ROM devices asdescribed above. In some embodiments, the computer system 2800 may alsoinclude a processing acceleration unit 2816, which can include a digitalsignal processor DSP, a special-purpose processor, and/or the like.

The computer-readable storage media reader 2812 can further be connectedto a computer-readable storage medium 2810, together (and, optionally,in combination with storage device(s) 2808) comprehensively representingremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information. Thecommunications system 2814 may permit data to be exchanged with thenetwork and/or any other computer described above with respect to thesystem 2800.

The computer system 2800 may also comprise software elements, shown asbeing currently located within a working memory 2818, including anoperating system 2820 and/or other code 2822, such as an applicationprogram (which may be a client application, Web browser, mid-tierapplication, RDBMS, etc.). It should be appreciated that alternateembodiments of a computer system 2800 may have numerous variations fromthat described above. For example, customized hardware might also beused and/or particular elements might be implemented in hardware,software (including portable software, such as applets), or both.Further, connection to other computing devices such as networkinput/output devices may be employed.

Storage media and computer readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information such as computer readable instructions, data structures,program modules, or other data, including RAM, ROM, EEPROM, flash memoryor other memory technology, CD-ROM, digital versatile disk (DVD) orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, data signals, datatransmissions, or any other medium which can be used to store ortransmit the desired information and which can be accessed by thecomputer. Based on the disclosure and teachings provided herein, aperson of ordinary skill in the art will appreciate other ways and/ormethods to implement the various embodiments.

In the foregoing specification, the invention is described withreference to specific embodiments thereof, but those skilled in the artwill recognize that the invention is not limited thereto. Variousfeatures and aspects of the above-described invention may be usedindividually or jointly. Further, the invention can be utilized in anynumber of environments and applications beyond those described hereinwithout departing from the broader spirit and scope of thespecification. The specification and drawings arc, accordingly, to beregarded as illustrative rather than restrictive.

We claim:
 1. A computerized method for selecting inventory pricing foran event at a venue, the method comprising: determining, by a computerprocessing unit, a rate at which a first inventory of seats have soldfor an event at a venue; calculating, by the computer processing unit, ademand for a second inventory of seats as a function of the rate atwhich the first inventory of seats sold, the seats of the first andsecond inventories being comparable in quality; and determining, by thecomputer processing unit based the calculated demand, a price level atwhich to sell the second inventory of seats.
 2. The computerized methodof claim 1, wherein the function is a hazard function.
 3. Thecomputerized method of claim 1, wherein the first inventory of seats issold for a different event than the second inventory of seats.
 4. Thecomputerized method of claim 1, wherein the first and second inventoriesof seats are in the same venue.
 5. A computerized method for selectinginventory pricing for an event at a venue, the method comprising:receiving a first sales status for a first plurality of seats for anevent at a first point in time, the first plurality of seats associatedwith a first price level; receiving a second sales status for the firstplurality of seats at a second point in time; comparing, by a computerprocessing unit, the first sales status and the second sales status todetermine a number of sold seats in the first plurality of seats;algorithmically predicting, by a computer processing unit, a demand fora second plurality of seats as a function of the number of sold seats inthe first plurality of seats; and selecting a second price level for thesecond plurality of seats, by a computer processing unit, based on thepredicted demand.
 6. The computerized method of claim 5, wherein thefunction is a hazard function.
 7. The computerized method of claim 6,wherein the function is a member selected from the group consisting of amonotonic algorithm, a non-monotonic algorithm, an exponential model, apower model, an exponential gamma model, a Weibull-gamma model, and acombination thereof.
 8. The computerized method of claim 5, wherein thefirst point in time is a starting on-sale time of the event.
 9. Thecomputerized method of claim 5, wherein the second price level is lowerin price than the first price level.
 10. The computerized method ofclaim 5, wherein the second price level is within a price plan.
 11. Thecomputerized method of claim 10, wherein the price plan has a tieredprice level.
 12. The computerized method of claim 5, wherein the demandis predicted at the first point in time.
 13. The computerized method ofclaim 6, wherein the demand is predicted at the second point in time.14. The computerized method of claim 5, wherein the demand is used toprepare an algorithm model.
 15. The computerized method of claim 14,wherein the algorithm model is used to predict demand for a futureevent.